[PYTHON] Changed benchmarking strategy. Instead of enqueueing many

kernels before synchronizing, the kernels are now  enqueued one by one.

This makes it possible to clear the L2 cache before running the
workload, and also potentially collect some variance data for error bars
in plots

python/triton/testing.py

@@ -1,18 +1,21 @@
 import torch
 import os
 
+
 def sparsify_tensor(x, mask, block):
     ret = torch.empty((x.size(0), mask.sum(), block, block), dtype=x.dtype, device=x.device)
     for idx, (h, i, j) in enumerate(zip(*mask.nonzero(as_tuple=True))):
         ret[:, idx, :, :] = x[:, h, i * block:(i + 1) * block, j * block:(j + 1) * block]
     return ret
 
+
 def mask_tensor(x, mask, block, value=0):
     ret = x.clone()
     for h, i, j in zip(*(mask == 0).nonzero(as_tuple=True)):
         ret[:, h, i * block:(i + 1) * block, j * block:(j + 1) * block] = value
     return ret
 
+
 def allclose(x, y):
     assert x.dtype == y.dtype
     diff = abs(x - y)
@@ -22,22 +25,37 @@ def allclose(x, y):
     err = torch.max(diff) / torch.max(x_max, y_max)
     return err < tol
 
-def do_bench(fn, flops=0, warmup=10, rep=50, grad_to_none=None):
+
+def do_bench(fn, warmup=10, rep=50, grad_to_none=None, clear_l2=False):
     start_event = torch.cuda.Event(enable_timing=True)
     end_event = torch.cuda.Event(enable_timing=True)
+    # warmup to put the clock in a stable regime
     ret = fn()
     for i in range(warmup):
         fn()
     torch.cuda.synchronize()
-    start_event.record()
+    total_ms = 0
+    # We maintain a buffer of 256 MB that we clear
+    # before each kernel call to make sure that the L2
+    # doesn't contain any input data before the run
+    cache = torch.empty(int(256e6), dtype=torch.int8, device='cuda')
     for i in range(rep):
+        # we don't want `fn` to accumulate gradient values
+        # if it contains a backward pass. So we clear the
+        # provided gradients
         if grad_to_none is not None:
             grad_to_none.grad = None
+        # reset L2
+        cache.zero_()
+        # record time of `fn`
+        start_event.record()
         fn()
         end_event.record()
         torch.cuda.synchronize()
-    time_ms = start_event.elapsed_time(end_event) / rep
+        total_ms += start_event.elapsed_time(end_event)
-    return time_ms
+    # return the average runtime of `fn`
+    return total_ms / rep
+
 
 class Benchmark:
     def __init__(self, x_names, x_vals, y_name, y_vals, y_lines, ylabel, loglog, plot_name, args):
@@ -51,6 +69,7 @@ class Benchmark:
         self.plot_name = plot_name
         self.args = args
 
+
 class Mark:
     def __init__(self, fn, benchmarks):
         self.fn = fn
@@ -85,6 +104,7 @@ class Mark:
                 html.write(f"<image src=\"{bench.plot_name}.png\"/>\n")
             html.write("</body></html>\n")
 
+
 def perf_report(benchmarks):
     wrapper = lambda fn: Mark(fn, benchmarks)
     return wrapper